Abstract

HandsDown is a novel technique for user identification on interactive surfaces. It enables users to access personal data on a shared surface, to associate objects with their identity, and to fluidly customize appearance, content, or functionality of the user interface. To identify, users put down their hand flat on the surface. HandsDown is based on hand contour analysis; neither user instrumentation nor external devices are required for identification. Characteristic features of the hand are initially extracted from images captured by the surface’s camera system and then classified using Support Vector Machines (SVM). We present a proof-of-concept implementation and show results of our evaluation which indicates the technique’s robustness for user identification within small groups. Additionally, we introduce a set of interaction techniques to illustrate how HandsDown can improve the user experience, and we discuss the design space of such interactions.